Complex inputs and outputs can be represented as vector embeddings generated by recursive neural nets without substantial accuracy dropoff, which enables future work on automating feedback for student learning on complex tasks.

On average, 9.2 people have a particular distinct set of food preferences, each represented by a community/cluster.

On average, only 2.43 of your friends share the same food preferences. This implies there are ~6 potential individuals we could recommend who would share your food tastes that you don't already know.

We have found a way to predict with 47% accuracy (far better than random chance, given there are 533 communities) whether or not two people have food tastes similar enough that they rate a given restaurant within 1 star of each other.